Subject Matter Expert Knowledge Mapping Using Dynamic Clustering

2. The computing system of claim 1, wherein the processor is configured to extract keywords from opinions and from data used to derive the opinions within the historical work orders.

3. The computing system of claim 1, wherein the processor is configured to execute the natural language processing algorithm on the historical work orders to extract sequences of words and score the sequences of words.

4. The computing system of claim 1, wherein the plurality of clusters are mapped to a plurality of different causes of failure of the asset, respectively.

5. The computing system of claim 1, wherein the method further comprises modifying the plurality of clusters based on user feedback provided via a user interface, and generating a modified knowledge mapping algorithm based on the user feedback.

6. The computing system of claim 1, wherein the natural language processing algorithm comprises a rapid automatic keyword extraction (RAKE) algorithm.

7. The computing system of claim 1, wherein the processor is further configured to receive a new work order, extract keywords from the new work order, and execute the generated knowledge mapping algorithm on the extracted keywords to determine a cause of failure associated with the new work order.

8. The computing system of claim 7, wherein the processor is configured to assign the new work order to a cluster from among the plurality of clusters via execution of the generated knowledge mapping algorithm.

10. The method of claim 9, wherein the extracting comprises extracting keywords from opinions and from data used to derive the opinions within the historical work orders.

11. The method of claim 9, wherein the extracting comprises executing the natural language processing algorithm on the historical work orders to extract sequences of words and score the sequences of words.

12. The method of claim 9, wherein the plurality of clusters are mapped to a plurality of different causes of failure of the asset, respectively.

13. The method of claim 9, wherein the method further comprises modifying the plurality of clusters based on user feedback provided via a user interface, and generating a modified knowledge mapping algorithm based on the user feedback.

14. The method of claim 9, wherein the natural language processing algorithm comprises a rapid automatic keyword extraction (RAKE) algorithm.

15. The method of claim 9, wherein the method further comprises receiving a new work order, extracting keywords from the new work order, and executing the generated knowledge mapping algorithm on the extracted keywords to determine a cause of failure associated with the new work order.

16. The method of claim 15, wherein the clustering comprises assigning the new work order to a cluster from among the plurality of clusters via execution of the generated knowledge mapping algorithm.

18. The non-transitory computer-readable medium of claim 17, wherein the extracting comprises extracting keywords from opinions and from data used to derive the opinions within the historical work orders.

19. The non-transitory computer-readable medium of claim 17, wherein the extracting comprises executing the natural language processing algorithm on the historical work orders to extract sequences of words and score the sequences of words.

20. The non-transitory computer-readable medium of claim 17, wherein the plurality of clusters are mapped to a plurality of different causes of failure of the asset, respectively.